The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning
Open accessAbstract
The retrosynthetic accessibility score (RAscore) is based on AI driven retrosynthetic planning, and is useful for rapid scoring of synthetic feasability and pre-screening of large datasets of virtual/generated molecules.
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Cites
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106
Planning chemical syntheses with deep neural networks and symbolic AI
Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature · doi:10.1038/nature25978 · Cited by 1,847
Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573
Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
Ruddigkeit, L., van Deursen, R., Blum, L. C., & Reymond, J.-L. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300415d · Cited by 1,505
Self-citationDeepTox: toxicity prediction using deep learning
Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081
GuacaMol: Benchmarking Models for de Novo Molecular Design
Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00839 · Cited by 895
REINVENT 2.0: An AI Tool for De Novo Drug Design
Blaschke, T., Arús-Pous, J., Chen, H., Margreitter, C., Tyrchan, C., Engkvist, O., Papadopoulos, K., & Patronov, A. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00915 · Cited by 496
Self-citation
Cited by
Generative Models as an Emerging Paradigm in the Chemical Sciences
Anstine, D. M., & Isayev, O. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c13467 · Cited by 313
Bayesian optimization with known experimental and design constraints for chemistry applications
Hickman, R. J., Aldeghi, M., Häse, F., & Aspuru-Guzik, A. (2022) · Digital Discovery · doi:10.1039/d2dd00028h · Cited by 98
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Unleashing the potential of cell painting assays for compound activities and hazards prediction
Odje, F., Meijer, D., von Coburg, E., van der Hooft, J. J. J., Dunst, S., Medema, M. H., & Volkamer, A. (2024) · Frontiers in Toxicology · doi:10.3389/ftox.2024.1401036 · Cited by 11
Metadata
- Authors
- Thakkar, A., Chadimová, V., Bjerrum, E. J., Engkvist, O., & Reymond, J.-L.
- Venue
- Chemical Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2021
- Volume
- 12
- Issue
- 9
- Pages
- 3339-3349
- ISSN
- 2041-6520 (ISSN-L)
- 2041-6539
- OpenAlex
- W3123775255
- Cited by
- 220
- References
- 7
- FWCI
- 14.12
- Type
- journal article
- Language
- en
- Open access
- diamond
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- CoChat. (2026). Citation check: Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning. https://app.cochat.ai/papers/10.1039/d0sc05401a
- The paper
- Thakkar, A., Chadimová, V., Bjerrum, E. J., Engkvist, O., & Reymond, J.-L. (2021). Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning. Chemical Science. https://doi.org/10.1039/d0sc05401a
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
